Filzbach is a flexible, fast, robust, parameter estimation engine that allows you to parameterize arbitrary, non-linear models, of the kind that are necessary in biological sciences, against multiple, heterogeneous data sets. Filzbach allows for Bayesian parameter estimation, maximum likelihood analysis, priors, latents, hierarchies, error propagation and model selection, often with just a few lines of code.
Traditionally, ecology and biology has been largely split into the purely empirical (generating or analysing data with only informal use of models) and the purely theoretical (analysing models that have been, at best, only informally constrained with data). However, to create a precise, predictive, understanding of ecological and biological systems it is necessary to bridge this gap, using data to formally parameterize, and select between, competing models.
- Specify parameters, define the likelihood, and Filzbach does the rest
- The automatic adaptive MCMC sampling algorithm copes with a wide range of different problems with no need for any manual tuning
- Automatic handling of multiple chains, testing for convergence, calculation of MLEs, posterior means and credible intervals on all parameters; and AIC, BIC, DIC
- Easy error propagation of parameter uncertainty through any model
- Fast and robust compared to commonly used alternatives
- Comes with a library of easy to use parameter distributions -- but can be extended include any others, so long as they are written in C
To try online, download, and learn more, see the Filzbach section of our new tool site.
- Mark C Vanderwel, David A Coomes, and Drew W Purves, Quantifying variation in forest disturbance, and its effects on aboveground biomass dynamics, across the eastern United States, in Global Change Biology, Wiley, January 2013
- Tim Newbold, Jorn P W Scharlemann, Stuart H M Butchart, Cagan A Sekercioglu, Rob Alkemade, Hollie Booth, and Drew W Purves, Ecological traits affect the response of tropical forest bird species to land-use intensity, in Proceedings of the Royal Society of London, Series B, The Royal Society, November 2012